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Abstract Recent progress in cassava transformation has allowed the robust production of transgenic cassava even under suboptimal plant tissue culture conditions. The transformation protocol has so far been used mostly for the cassava model cultivar 60444 because of its good regeneration capacity of embryogenic tissues. However, for deployment and adoption of transgenic cassava in the field it is important to develop robust transformation methods for farmer- and industry-preferred landraces and cultivars. Because dynamics of multiplication and regeneration of embryogenic tissues differ between cassava genotypes, it was necessary to adapt the efficient cv. 60444 transformation protocol to genotypes that are more recalcitrant to transformation. Here we demonstrate that an improved cassava transformation protocol for cv. 60444 could be successfully modified for production of transgenic farmer-preferred cassava landraces. The modified transformation method reports on procedures for optimization and is likely transferable to other cassava genotypes reportedly recalcitrant to transformation provided production of high quality FEC. Because the three farmer-preferred cassava landraces selected in this study have been identified as resistant or tolerant to cassava mosaic disease (CMD), the adapted protocol will be essential to mobilize improved traits into cassava genotypes suitable for regions where CMD limits production.

Abstract Background Renal cell carcinoma (RCC) is characterized by a number of diverse molecular aberrations that differ among individuals. Recent approaches to molecularly classify RCC were based on clinical, pathological as well as on single molecular parameters. As a consequence, gene expression patterns reflecting the sum of genetic aberrations in individual tumors may not have been recognized. In an attempt to uncover such molecular features in RCC, we used a novel, unbiased and integrative approach. Methods We integrated gene expression data from 97 primary RCC of different pathologic parameters, 15 RCC metastases as well as 34 cancer cell lines for two-way nonsupervised hierarchical clustering using gene groups suggested by the PANTHER Classification System. We depicted the genomic landscape of the resulted tumor groups by means of Single Nuclear Polymorphism (SNP) technology. Finally, the achieved results were immunohistochemically analyzed using a tissue microarray (TMA) composed of 254 RCC. Results We found robust, genome wide expression signatures, which split RCC into three distinct molecular subgroups. These groups remained stable even if randomly selected gene sets were clustered. Notably, the pattern obtained from RCC cell lines was clearly distinguishable from that of primary tumors. SNP array analysis demonstrated differing frequencies of chromosomal copy number alterations among RCC subgroups. TMA analysis with group-specific markers showed a prognostic significance of the different groups. Conclusion We propose the existence of characteristic and histologically independent genome-wide expression outputs in RCC with potential biological and clinical relevance.

Abstract Microarrays are routine tools for transcript profiling, and genomic tiling arrays such as the Arabidopsis AGRONOMICS1 arrays have been found to be highly suitable for such experiments because changes in genome annotation can be easily integrated at the data analysis level. In a transcript profiling experiment, RNA labeling is a critical step, most often initiated by oligo-dT-primed reverse transcription. Although this has been found to be a robust and reliable method, very long transcripts or non-polyadenylated transcripts might be labeled inefficiently. In this study, we first provide data handling methods to analyze AGRONOMICS1 tiling microarrays based on the TAIR10 genome annotation. Second, we describe methods to easily quantify antisense transcripts on such tiling arrays. Third, we test a random-primed RNA labeling method, and find that on AGRONOMICS1 arrays this method has similar general performance as the conventional oligo-dT-primed method. In contrast to the latter, however, the former works considerably better for long transcripts and for non-polyadenylated transcripts such as found in mitochondria and plastids. We propose that researchers interested in organelle function use the random-primed method to unleash the full potential of genomic tiling arrays.

Abstract Background RT-qPCR is a sensitive and increasingly used method for gene expression quantification. To normalize RT-qPCR measurements between samples, most laboratories use endogenous reference genes as internal controls. There is increasing evidence, however, that the expression of commonly used reference genes can vary significantly in certain contexts. Results Using the Genevestigator database of normalized and well-annotated microarray experiments, we describe the expression stability characteristics of the transciptomes of several organisms. The results show that a) no genes are universally stable, b) most commonly used reference genes yield very high transcript abundances as compared to the entire transcriptome, and c) for each biological context a subset of stable genes exists that has smaller variance than commonly used reference genes or genes that were selected for their stability across all conditions. Conclusion We therefore propose the normalization of RT-qPCR data using reference genes that are specifically chosen for the conditions under study. RefGenes is a community tool developed for that purpose. Validation RT-qPCR experiments across several organisms showed that the candidates proposed by RefGenes generally outperformed commonly used reference genes. RefGenes is available within Genevestigator at http://www.genevestigator.com.

Abstract Background Chromatin dynamics and stability are both required to control normal development of multicellular organisms. Chromatin assembly factor CAF-1 is a histone chaperone that facilitates chromatin formation and the maintenance of specific chromatin states. In plants and animals CAF-1 is essential for normal development, but it is poorly understood which developmental pathways require CAF-1 function. Results Mutations in all three CAF-1 subunits affect Arabidopsis trichome morphology and lack of CAF-1 function results in formation of trichomes with supernumerary branches. This phenotype can be partially alleviated by external sucrose. In contrast, other aspects of the CAF-1 mutant phenotype, such as defective meristem function and organ formation, are aggravated by external sucrose. Double mutant analyses revealed epistatic interactions between CAF-1 mutants and stichel, but non-epistatic interactions between CAF-1 mutants and glabra3 and kaktus. In addition, mutations in CAF-1 could partly suppress the strong overbranching and polyploidization phenotype of kaktus mutants. Conclusion CAF-1 is required for cell differentiation and regulates trichome development together with STICHEL in an endoreduplication-independent pathway. This function of CAF-1 can be partially substituted by application of exogenous sucrose. Finally, CAF-1 is also needed for the high degree of endoreduplication in kaktus mutants and thus for the realization of kaktus' extreme overbranching phenotype.

Abstract Background Research in plant science laboratories often involves usage of many different species, cultivars, ecotypes, mutants, alleles or transgenic lines. This creates a great challenge to keep track of the identity of experimental plants and stored samples or seeds. Results Here, we describe PlantDB – a Microsoft® Office Access database – with a user-friendly front-end for managing information relevant for experimental plants. PlantDB can hold information about plants of different species, cultivars or genetic composition. Introduction of a concise identifier system allows easy generation of pedigree trees. In addition, all information about any experimental plant – from growth conditions and dates over extracted samples such as RNA to files containing images of the plants – can be linked unequivocally. Conclusion We have been using PlantDB for several years in our laboratory and found that it greatly facilitates access to relevant information.

Abstract Background Gene function analysis often requires a complex and laborious sequence of laboratory and computer-based experiments. Choosing an effective experimental design generally results from hypotheses derived from prior knowledge or experimentation. Knowledge obtained from meta-analyzing compendia of expression data with annotation libraries can provide significant clues in understanding gene and network function, resulting in better hypotheses that can be tested in the laboratory. Description Genevestigator is a microarray database and analysis system allowing context-driven queries. Simple but powerful tools allow biologists with little computational background to retrieve information about when, where and how genes are expressed. We manually curated and quality-controlled 3110 mouse Affymetrix arrays from public repositories. Data queries can be run against an annotation library comprising 160 anatomy categories, 12 developmental stage groups, 80 stimuli, and 182 genetic backgrounds or modifications. The quality of results obtained through Genevestigator is illustrated by a number of biological scenarios that are substantiated by other types of experimentation in the literature. Conclusion The Genevestigator-Mouse database effectively provides biologically meaningful results and can be accessed at https://www.genevestigator.ethz.ch.

Abstract Appropriate biological interpretation of microarray data calls for relevant experimental annotation. The widely accepted MIAME guidelines provide a generic, organism-independant standard for minimal information about microarray experiments. In its overall structure, MIAME is very general and specifications cover mostly technical aspects, while relevant organism-specific information useful to understand the underlying experiments is largely missing. If plant biologists want to use results from published microarray experiments, they need detailed information about biological aspects, such as growth conditions, harvesting time or harvested organ(s). Here, we propose MIAME/Plant, a standard describing which biological details to be captured for describing microarray experiments involving plants. We expect that a more detailed and more systematic annotation of microarray experiments will greatly increase the use of transcriptome data sets for the scientific community. The power and value of systematic annotation of microarray data is convincingly demonstrated by data warehouses such as Genevestigator® or NASCArrays, and better experimental annotation will make these applications even more powerful.